A genetic algorithm based approach for robust evaluation of form tolerances

Sharma, Rohit; Rajagopal, Karthik; Anand, Sam

doi:10.1016/s0278-6125(00)88889-5

Cited by 37 publications

(17 citation statements)

References 12 publications

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“…We use a GA in order to apply the MZT data-fitting algorithm [16] [17] [21]. A GA to solve the optimization problem (6) for a two-dimensional search space entails a population of chromosomes made of pairs of coordinates (their genes).…”

Section: Ga For Roundness Error Evaluationmentioning

confidence: 99%

“…Another approach is based on the Voronoi diagram as described by Roy and Zhang [15]; the method yields a very accurate measurement of the roundness error, but it is computationally intensive. As for genetic algorithms (GAs) to find the solution of the MZT problem, as proposed in this paper, Sharma et al [16] used a standard GA for the evaluation of multiple form tolerance classes such as straightness, flatness, roundness, and cylindricity. There was no need to optimize the algorithm performance, choosing the parameters involved in the computation, because of the small dataset size (up to 100 datapoints).…”

Section: Introductionmentioning

confidence: 99%

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Fast genetic algorithm for roundness evaluation by the minimum zone tolerance (MZT) method

et al. 2011

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The main goal of the minimum zone tolerance (MZT) method is to achieve the best estimation of the roundness error, but it is computationally intensive. This paper describes the application of a genetic algorithm (GA) to minimize the computation time in the evaluation of CMM roundness errors of a large cloud of sampled datapoints (0.2° equally spaced datapoints). Computational experiments have shown that by selecting the optimal GA parameters, namely a combination of the four genetic parameters related to population size, crossover, mutation, and stop conditions, the computation time can be reduced by up to one order of magnitude, allowing realtime operation. Optimization has been tested using seven CMM datasets, obtained from different machining features, and compared with the LSQ method. The performance of the optimized algorithm has been validated with GA from the literature using four benchmark datasets.

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Section: Ga For Roundness Error Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast genetic algorithm for roundness evaluation by the minimum zone tolerance (MZT) method

et al. 2011

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“…The first approach is, in general, very computationally expensive, especially, when the number of data points is high. One of these methods is based on the Voronoi diagram [3]. Considering the trend towards higher number of acquired data points, in the order of thousands, made possible by optical methods or CMM scanning, this work addresses the latter approach: non-linear optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Among optimization techniques are Chebyshev approximation [4] and simplex search [5]. Metaheuristics approaches are also available in the literature, such as particle swarm optimization (PSO) [6], linear approximation [7], and genetic algorithms (GAs) [3], [8], [9], [10] and [11]. Performance of methods has been reviewed in [12].…”

Section: Introductionmentioning

confidence: 99%

Optimum Dataset Size and Search Space for Minimum Zone Roundness Evaluation by Genetic Algorithm

Meo¹,

Profumo²,

Rossi³

et al. 2013

Measurement Science Review

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Roundness is one of the most common features in machining. The minimum zone tolerance (MZT) approach provides the minimum roundness error, i.e. the minimum distance between the two concentric reference circles containing the acquired profile; more accurate form error estimation results in less false part rejections. MZT is still an open problem and is approached here by a Genetic Algorithm. Only few authors have addressed the definition of the search space center and size and its relationship with the dataset size, which greatly influence the inspection time for the profile measurement and the convergence speed of the roundness estimation algorithm for a given target accuracy. Experimental tests on certified roundness profiles, using the profile centroid as the search space center, have shown that the search space size is related to the number of dataset points and an optimum exists, which provides a computation time reduction up to an order of magnitude.

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“…Many researchers have cast the roundness evaluation as the solution of a general optimization problem (Choi and Kurfess, 1999;Gou et al, 1999;Sharma et al, 2000;Weber et al, 2002;Yau and Menq, 1996). To get a value that is close to the global minimum, some researchers addressed the initial conditions and adopted coordinate transformation techniques (Endrias and Feng, 2003;Lai and Chen, 1996) or suggested approximating orthogonal residuals by functions that are linear in the feature parameters (Shunmugam, 1991).…”

Section: Introductionmentioning

confidence: 99%